Energy conservation in Wireless Sensor Networks Sagnik Bhattacharya, Tarek Abdelzaher

University of Virginia, Department of Computer Science School of Engineering and Applied Science, Charlottesville, VA 22903

http://www.cs.virginia.edu/nest

Tiny Sensor NodesTowards efficient Wireless Sensor NetworksSensor Network Applications

National Science

Foundation

Office of Naval Research

DARPA ITO

In recent years, wireless sensor networks have emerged as a new fast-growing application domain for distributed computing . The vision of sensor networks presents new and unique challenges arising from the highly constrained resources of individual sensor-equipped nodes, and the large scale of the overall network. Power is identified as the most expensive resource in a sensor network. In most cases, such networks are meant for one-time use, i.e., once the battery dies, the node dies too. Hence, maximizing network lifetime by conserving power is a matter of great importance. Our approach is to develop middleware to conserve energy and give the base-stations the abstractions of a network virtual machine embedded in the network.

•4 Mhz, 8 bit MCU (Amtel), 512 bytes RAM, 8K ROM

•900 Mhz radio (RF Monolithics) 10-100 ft. range

Data Placement and Replication MiddlewareMiddleware for Sensor Networks

MAC / Wireless Broadcast communication

Data Placement / Replication

In-network aggregation / Group

Consensus

Sensor Interface

Power Management

Middleware

Application

Location Service Location-aware Routing

The sensor nodes run on a battery which limits their lifetime. Experimental results have shown that wireless packet communication consumes most of the energy of a mode. Hence reducing the amount of communication shall result in increased network lifetime. The middleware allows the sensor nodes to conserve energy in an aggregated and distributed manner. The main attributes of the middleware are: Energy Conservation, Scalability, Portability, Consistency, Reduced Communication Overhead, Decentralized computation and Resource Allocation.

Event detection by in-network aggregation

Inspired by web caching and multicast technologies, our data placement middleware creates and places replicas of requested data so that communication is minimized. According to the request rates and the update rates of the data a hierarchy of replicas are created such that the depth of the hierarchy adapts with change in request and update rates.

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- Level-i copy

- Sensor

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In general, when the environment is placid and the update rate is low, more copies are created, and when the environment is volatile and the update rate is high, consolidation takes place and the number of copies is reduced. In general the depth of the copy tree is equal to Rmax / Rupdate.

Routing / Location service

Data Placement Middleware

ApplicationCopy/Sensor

data(at sensor)

Copy table(at sensor)

CacheCopy table

Redirect table

(at base-station)

Message Handlers

The data placement middleware causes the location-directed queries from the application to be redirected to the copy locations using the redirect table. These redirect queries get served by the copies which are stored in the cache at certain locations.

A tree of replicas of the sensor data is formed, and in general a base-station with a request rate R gets served by a level-R / Rupdate copy. Whenever an update occurs

it gets propagated along the copy tree.

Experimental Results

Our experimental results (using ns-2 simulator) show that our data placement strategy provides considerable energy savings and is quite close to the optimal omniscient multicast for the case when the request rates are greater than the update rate, but does not show the degradation in performance shown by omniscient multicast when

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Data Placement

No Data Placement

Omniscient Multicast

the update rate becomes greater. It gracefully consolidates the copies as the update rate increases, until it reaches a point when no copies are created.

When an event such as an explosion occurs, individual sensor values like temperature, light etc, by themselves may not be of interest to the applications, but taken together, the macro-information (i.e., explosion) is much more relevant. We use dynamically-programmable rules, distributed arbitrarily in the sensor network to identify and triangulate the location of the events.

Event Cluster Formation

Single Value

Aggregation

Rule Evaluation

Data Aggregation

Example of a rule: (Temperature>500) and (Light>100) => Explosion. If (T= 600) and (L=200) then Explosion = true and confidence = (600-500)/500 + (200-100)/100 = 1.2

At this step, clusters are formed dynamically within the region where the sensors pick up abnormal values, and a cluster leader is elected. No node can belong to more than one cluster.

At this stage all the sensors of a single type (e.g.., temperature) come to a consensus so that each node in a cluster has exactly the same value for different types of sensors.

At this stage, clusters evaluate the rules among themselves. Each node may not have the necessary rule which evaluates to true given the data, but eventually the identified event and confidence is transmitted to the cluster leader.

In the final stage, the cluster leaders communicate amongst themselves and use the confidence values of the individual clusters to pinpoint the location of the events. The final data about the event type and location are propagated to the base-stations